機器學習和深度學習技術已成為解決各領域複雜挑戰的強大工具。這些方法之所以強大，是因為它們能夠從大型複雜數據集中提取模式和洞察，自動化決策過程，並隨著時間的推移不斷改進。它們使我們能夠觀察並量化普通人難以捕捉的數據模式，從而獲得更深入的見解和更準確的預測。

本論文介紹了兩篇研究論文，這些論文利用這些方法解決了神經影像學和計算機視覺中量化人類健康的兩個不同但相互關聯的問題。

第一項研究《Age prediction using resting-state functional MRI》解決了理解大腦老化的挑戰。通過在靜息態功能磁共振成像（rsfMRI）數據上使用最小絕對收縮和選擇算子（LASSO），我們確定了與大腦年齡最相關的預測性連接。在包含176名健康志願者的研究中，我們建立了一個參考模型，該模型顯著降低了預測誤差，並識別出異常老化模式，尤其是在默認模式網絡（DMN）中。研究中識別出39個預測性連接，並達到了2.48年的留一法平均絕對誤差。值得注意的是，我們的正常參考模型在幾乎所有成年受試者的預測中達到了已發表模型中最低的誤差，突出了與正常老化相關的預測性連接。這項工作對神經退行性疾病的早期檢測具有重要意義，提供了一種在認知症狀出現之前識別異常大腦老化的非侵入性方法，可能促進更早的干預和個性化治療策略。

第二項研究《Skin feature point tracking using deep feature encodings》探討了用於健康監測應用的計算機視覺的進展。我們提出了一種新穎的流程，使用卷積堆疊自編碼器追蹤面部和皮膚特徵，這對於在心搏圖和帕金森病的運動退化分析中準確估算心率至關重要。我們的方法實現了0.6-3.3像素的追蹤誤差，在幾乎所有場景中優於傳統算法如SIFT、SURF和LK。此外，我們的方法是唯一未出現發散的，並且在大運動情況下相比最新的特徵匹配變壓器Omnimotion表現出更優越的性能。這項工作對改進非侵入性健康監測系統具有重要意義，提供了更準確的工具來追蹤微小的運動和心血管變化，從而實現帕金森病和心臟病等疾病的早期診斷和精確監測。

這兩項研究共同展示了預測建模和深度學習對推進神經影像學和計算機視覺的影響。通過利用功能磁共振成像實現大腦異常老化的早期檢測，同時提升計算機視覺在追蹤運動功能微小變化中的能力，提供更準確、非侵入性的工具，用於神經退行性疾病和心血管疾病的診斷、量化和監測。

Machine learning and deep learning techniques have emerged as powerful tools for addressing complex challenges across diverse domains. These methodologies are powerful because they extract patterns and insights from large and complex datasets, automate decision-making processes, and continuously improve over time. They enable us to observe and quantify patterns in data that a normal human would not be able to capture, leading to deeper insights and more accurate predictions. This dissertation presents two research papers that leverage these methodologies to tackle distinct yet interconnected problems in neuroimaging and computer vision for the quantification of human health.

The first investigation, ``Age prediction using resting-state functional MRI," addresses the challenge of understanding brain aging. By employing the Least Absolute Shrinkage and Selection Operator (LASSO) on resting-state functional MRI (rsfMRI) data, we identify the most predictive correlations related to brain age. Our study, involving a cohort of 176 healthy volunteers, establishes a reference model that significantly reduces prediction errors and identifies abnormal aging patterns, particularly within the Default Mode Network (DMN). This study identifies 39 predictive correlations and achieves a leave-one-out mean absolute error of 2.48 years. Remarkably, our normal reference model attains the lowest prediction error among published models evaluated on adult subjects of almost all ages, highlighting correlations predictive of normal aging. The implications of this work extend to early detection of neurodegenerative diseases, providing a non-invasive method to identify abnormal brain aging before cognitive symptoms manifest, potentially allowing for earlier interventions and personalized treatment strategies.

The second investigation, ``Skin feature point tracking using deep feature encodings," explores advancements in computer vision for health monitoring applications. We propose a novel pipeline using a convolutional stacked autoencoder to track facial and skin features, which are crucial for accurate heart rate estimation in ballistocardiography and motor degradation analysis in Parkinson's disease. Our method achieves tracking errors as low as 0.6-3.3 pixels, outperforming traditional algorithms like SIFT, SURF, and LK in almost all scenarios. Additionally, our approach is the only one that did not diverge and demonstrated superior performance compared to the latest state-of-the-art transformer for feature matching-- Omnimotion, especially under conditions of large motion.The implications of this work extend to improving non-invasive health monitoring systems, offering more accurate tools for tracking subtle motor and cardiovascular changes, and enabling early diagnosis and precise monitoring of diseases like Parkinson's and heart conditions.

Together, these studies demonstrate the impact of predictive modeling and deep learning on advancing our understanding and capabilities in neuroimaging and computer vision. This is achieved by advancing neuroimaging by enabling the early detection of abnormal brain aging via functional MRI, while also improving computer vision capabilities for tracking subtle changes in motor functions, offering more accurate, non-invasive tools for diagnosing, quantifying, and monitoring neurodegenerative and cardiovascular diseases.

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